Traditional Sleep Structure Indexes (TSSIs) are insufficient to identify patterns of altered sleep. TSSIs mainly account for absolute time measures, but different levels of state instability may lead to similar absolute time distribution. Therefore, sleep stability remains beyond the scope of TSSIs. However, recent studies suggest that sleep disorders may be rather influenced by a breakdown in the sleep-stage switching mechanisms. In this study, we propose a set of 11 Sleep Transition Indexes (STIs) that characterize sleep fragmentation and account for the state-stability governed by the ultradian, homeostatic and circadian rhythms. We demonstrate that most of the proposed STIs are potential markers of SAHS severity, while TSSIs are not. In addition, we provide a new framework to analyze sleep disorders from the direct perspective of sleep regulatory mechanisms. In particular, our results indicate that SAHS may be influenced by a dysregulation of homeostatic rhythms but not of ultradian or circadian rhythms.

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH/sub adaptive/ and TH/sub median/) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index)<5h/sup -1/, AHI<10 h/sup -1/, AHI<15h/sup -1/, AHI<30h/sup -1/). Results showed that TH/sub adaptive/ outperformed TH/sub median/ on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.